chore: import upstream snapshot with attribution

tests/model_identification/test_identification.py

@@ -0,0 +1,109 @@
+import json
+from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+from pprint import pformat
+from typing import Any
+
+import pytest
+
+from invokeai.backend.model_manager.configs.controlnet import ControlAdapterDefaultSettings
+from invokeai.backend.model_manager.configs.factory import (
+    ModelConfigFactory,
+)
+from invokeai.backend.model_manager.configs.main import MainModelDefaultSettings
+from invokeai.backend.model_manager.taxonomy import (
+    BaseModelType,
+)
+from invokeai.backend.util.logging import InvokeAILogger
+from tests.model_identification.stripped_model_on_disk import StrippedModelOnDisk
+
+logger = InvokeAILogger.get_logger(__file__)
+
+
+@pytest.mark.parametrize(
+    "model_name,preprocessor",
+    [
+        ("some_canny_model", "canny_image_processor"),
+        ("some_depth_model", "depth_anything_image_processor"),
+        ("some_pose_model", "dw_openpose_image_processor"),
+        ("i like turtles", None),
+    ],
+)
+def test_controlnet_t2i_default_settings(model_name: str, preprocessor: str | None):
+    assert ControlAdapterDefaultSettings.from_model_name(model_name).preprocessor == preprocessor
+
+
+@pytest.mark.parametrize(
+    "base,attrs",
+    [
+        (BaseModelType.StableDiffusion1, {"width": 512, "height": 512}),
+        (BaseModelType.StableDiffusion2, {"width": 768, "height": 768}),
+        (BaseModelType.StableDiffusionXL, {"width": 1024, "height": 1024}),
+        (BaseModelType.StableDiffusionXLRefiner, None),
+        (BaseModelType.Any, None),
+    ],
+)
+def test_default_settings_main(base: BaseModelType, attrs: dict[str, Any] | None):
+    settings = MainModelDefaultSettings.from_base(base)
+    if attrs is None:
+        assert settings is None
+    else:
+        for key, value in attrs.items():
+            assert getattr(settings, key) == value
+
+
+@dataclass
+class ModelAttributeMismatch:
+    key: str
+    expected: Any
+    actual: Any
+
+    def __str__(self) -> str:
+        return f"{self.key} expected {self.expected}, got {self.actual}"
+
+
+def _get_model_paths(datadir: Path) -> list[Path]:
+    """Helper to collect model paths for parameterization."""
+    return [p for p in (datadir / "stripped_models").iterdir() if p.is_dir()]
+
+
+@pytest.mark.parametrize("model_path", _get_model_paths(Path(__file__).parent))
+def test_model_identification(model_path: Path):
+    """Verifies results from ModelConfigBase.classify are consistent with those from ModelProbe.probe.
+    The test paths are gathered from the 'test_model_probe' directory.
+    """
+    id = model_path.name
+    test_metadata_path = model_path / "__test_metadata__.json"
+    test_metadata = json.loads(test_metadata_path.read_text())
+
+    if file_name := test_metadata.get("file_name", ""):
+        model_path = model_path / file_name
+
+    mod = StrippedModelOnDisk(model_path)
+
+    override_fields = test_metadata.get("override_fields", None)
+
+    try:
+        result = ModelConfigFactory.from_model_on_disk(mod, override_fields, allow_unknown=False)
+    except Exception as e:
+        print(mod.path)
+        pytest.fail(f"{id}: Exception during model probing: {e}")
+
+    if result.config is None:
+        pytest.fail(f"{id}: no match, detailed results:\n{pformat(result.details)}")
+
+    config = result.config
+
+    mismatched_attrs: list[ModelAttributeMismatch] = []
+
+    for key, expected_value in test_metadata["expected_config_attrs"].items():
+        actual_value = getattr(config, key)
+        if isinstance(actual_value, Enum):
+            actual_value = actual_value.value
+        if actual_value != expected_value:
+            mismatched_attrs.append(ModelAttributeMismatch(key, expected_value, actual_value))
+
+    if mismatched_attrs:
+        msg = "; ".join(str(m) for m in mismatched_attrs)
+        pytest.fail(f"{id}: {msg}")